





'''simple docstring'''

from __future__ import annotations

def     __magic_name__							(				__UpperCAmelCase   )    ->       bool:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							str(__UpperCAmelCase   )
   return n == n[::-1]

def     __magic_name__							(				__UpperCAmelCase = 1000000   )    ->       str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							0

   for i in range(1				,						__UpperCAmelCase   ):
      if is_palindrome(__UpperCAmelCase   ) and is_palindrome(bin(__UpperCAmelCase   ).split("""b"""   )[1]   ):
         total += i
   return total


if __name__ == "__main__":
    print(solution(int(str(input().strip()))))






import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
_SCREAMING_SNAKE_CASE 		=      logging.getLogger()

_SCREAMING_SNAKE_CASE 		=      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						List[str]				):





    """simple docstring"""


    os.makedirs(lowerCamelCase_   ,   exist_ok=lowerCamelCase_				)
    UpperCamelCase			  =      {"""source""": """What is love ?""", """target""": """life"""}
    UpperCamelCase			  =      {"""train""": 12, """val""": 2, """test""": 2}
    for split in ["train", "test", "val"]:
     for field in ["source", "target"]:
      UpperCamelCase			  =      """\n""".join([contents[field]] * n_lines[split]				)
      with open(os.path.join(lowerCamelCase_   ,   f"""{split}.{field}"""				)   ,   """w"""				) as f:
       f.write(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Tuple   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						str = "pytorch"				):





    """simple docstring"""


    UpperCamelCase			  =      self.get_auto_remove_tmp_dir()
    UpperCamelCase			  =      os.path.join(lowerCamelCase_   ,   """output"""				)
    UpperCamelCase			  =      os.path.join(lowerCamelCase_   ,   """data"""				)
    self._create_dummy_data(data_dir=lowerCamelCase_				)

    UpperCamelCase			  =      f"""
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            """.split()

    if gpus > 0:
     testargs.append(f"""--gpus={gpus}"""				)
     if is_apex_available():
      testargs.append("""--fp16"""				)
    else:
     testargs.append("""--gpus=0"""				)
     testargs.append("""--distributed_backend=ddp_cpu"""				)
     testargs.append("""--num_processes=2"""				)

    UpperCamelCase			  =      [sys.executable, str(Path(finetune_rag.__file__				).resolve()				)] + testargs
    execute_subprocess_async(lowerCamelCase_   ,   env=self.get_env()				)

    UpperCamelCase			  =      os.path.join(lowerCamelCase_   ,   """metrics.json"""				)
    with open(lowerCamelCase_				) as f:
     UpperCamelCase			  =      json.load(lowerCamelCase_				)
    return result







   @require_torch_gpu
   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      self._run_finetune(gpus=1				)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]   ,   0.2				)







   @require_torch_multi_gpu
   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self._run_finetune(gpus=2				)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]   ,   0.2				)







   @require_torch_gpu
   @require_ray
   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self._run_finetune(gpus=1   ,   distributed_retriever="""ray"""				)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]   ,   0.2				)







   @require_torch_multi_gpu
   @require_ray
   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      self._run_finetune(gpus=1   ,   distributed_retriever="""ray"""				)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]   ,   0.2				)
